{"id":{"repo_id":"penn","oai_identifier":"oai:repository.upenn.edu:20.500.14332/49309"},"canonical_url":"https://search.dev.ndltd.org/etd/penn/oai:repository.upenn.edu:20.500.14332/49309","repository":{"repo_id":"penn","name":"University of Pennsylvania","base_url":"https://repository.upenn.edu/server/oai/request"},"display":{"title":"Forecasting Event Ticket Sales","abstract":"This paper examines ticket sales data from events held at a large entertainment venue to develop a model that forecasts ticket sales. Data from thirteen different events are used, and the model chosen is a timing model which draws from two Weibull segments and clusters events into 2 separate groups. The paper provides some background into why forecasting ticket sales is a critical element of event planning. It then examines the data available to determine the specific modeling needs. Finally, the model approach is presented and the results of the chosen model are shown.","abstract_html":"This paper examines ticket sales data from events held at a large entertainment venue to develop a model that forecasts ticket sales. Data from thirteen different events are used, and the model chosen is a timing model which draws from two Weibull segments and clusters events into 2 separate groups. The paper provides some background into why forecasting ticket sales is a critical element of event planning. It then examines the data available to determine the specific modeling needs. Finally, the model approach is presented and the results of the chosen model are shown.","abstract_has_math":false,"creators":["Suher, Jacob"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008-05-01","date_published":"2008-05-01","updated_at":"2026-07-24T03:46:00Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repository.upenn.edu/handle/20.500.14332/49309","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Suher, Jacob"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05-17T02:27:56.000"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-05-23T04:48:36Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-05-23T04:48:36Z"]},{"key":"dc:date.issued","label":"Date","values":["2008-05-01"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation/Thesis"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://repository.upenn.edu/handle/20.500.14332/49309"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This paper examines ticket sales data from events held at a large entertainment venue to develop a model that forecasts ticket sales. Data from thirteen different events are used, and the model chosen is a timing model which draws from two Weibull segments and clusters events into 2 separate groups. The paper provides some background into why forecasting ticket sales is a critical element of event planning. It then examines the data available to determine the specific modeling needs. Finally, the model approach is presented and the results of the chosen model are shown."]},{"key":"dc:title","label":"Title","values":["Forecasting Event Ticket Sales"]}]}],"canonical_facts":{"dc:creator":["Suher, Jacob"],"dc:date":["2023-05-17T02:27:56.000"],"dc:date.accessioned":["2023-05-23T04:48:36Z"],"dc:date.available":["2023-05-23T04:48:36Z"],"dc:date.issued":["2008-05-01"],"dc:description.abstract":["This paper examines ticket sales data from events held at a large entertainment venue to develop a model that forecasts ticket sales. Data from thirteen different events are used, and the model chosen is a timing model which draws from two Weibull segments and clusters events into 2 separate groups. The paper provides some background into why forecasting ticket sales is a critical element of event planning. It then examines the data available to determine the specific modeling needs. Finally, the model approach is presented and the results of the chosen model are shown."],"dc:identifier.uri":["https://repository.upenn.edu/handle/20.500.14332/49309"],"dc:title":["Forecasting Event Ticket Sales"],"dc:type":["Dissertation/Thesis"]},"updated_at":"2026-07-24T03:46:00Z"}